code: def initialise_dataset_loader(
      self, data_param=None, task_param=None, data_partitioner=None
  ):
      self.data_param = data_param
      self.segmentation_param = task_param
  
      try:
          reader_phase = self.action_param.dataset_to_infer
      except AttributeError:
          reader_phase = None
      file_lists = data_partitioner.get_file_lists_by(
          phase=reader_phase, action=self.action
      )
      # read each line of csv files into an instance of Subject
      if self.is_training:
          self.readers = []
          for file_list in file_lists:
              reader = ImageReader(SUPPORTED_INPUT)
              reader.initialise(data_param, task_param, file_list)
              self.readers.append(reader)
  
      else:  # in the inference process use image input only
          inference_reader = ImageReader(["image"])
          inference_reader.initialise(
              data_param, task_param, file_lists[0]
          )
          self.readers = [inference_reader]
  
      foreground_masking_layer = None
      if self.net_param.normalise_foreground_only:
          foreground_masking_layer = BinaryMaskingLayer(
              type_str=self.net_param.foreground_type,
              multimod_fusion=self.net_param.multimod_foreground_type,
              threshold=0.0,
          )
  
      mean_var_normaliser = MeanVarNormalisationLayer(
          image_name="image",
          binary_masking_func=foreground_masking_layer,
      )
      histogram_normaliser = None
      if self.net_param.histogram_ref_file:
          histogram_normaliser = HistogramNormalisationLayer(
              image_name="image",
              modalities=vars(task_param).get("image"),
              model_filename=self.net_param.histogram_ref_file,
              binary_masking_func=foreground_masking_layer,
              norm_type=self.net_param.norm_type,
              cutoff=self.net_param.cutoff,
              name="hist_norm_layer",
          )
  
      label_normaliser = None
      if self.net_param.histogram_ref_file:
          label_normaliser = DiscreteLabelNormalisationLayer(
              image_name="label",
              modalities=vars(task_param).get("label"),
              model_filename=self.net_param.histogram_ref_file,
          )
  
      normalisation_layers = []
      if self.net_param.normalisation:
          normalisation_layers.append(histogram_normaliser)
      if self.net_param.whitening:
          normalisation_layers.append(mean_var_normaliser)
      if task_param.label_normalisation:
          normalisation_layers.append(label_normaliser)
  
      augmentation_layers = []
      if self.is_training:
          if self.action_param.random_flipping_axes != -1:
              augmentation_layers.append(
                  RandomFlipLayer(
                      flip_axes=self.action_param.random_flipping_axes
                  )
              )
          if self.action_param.scaling_percentage:
              augmentation_layers.append(
                  RandomSpatialScalingLayer(
                      min_percentage=self.action_param.scaling_percentage[
                          0
                      ],
                      max_percentage=self.action_param.scaling_percentage[
                          1
                      ],
                  )
              )
          if (
              self.action_param.rotation_angle
              or self.action_param.rotation_angle_x
              or self.action_param.rotation_angle_y
              or self.action_param.rotation_angle_z
          ):
              rotation_layer = RandomRotationLayer()
              if self.action_param.rotation_angle:
                  rotation_layer.init_uniform_angle(
                      self.action_param.rotation_angle
                  )
              else:
                  rotation_layer.init_non_uniform_angle(
                      self.action_param.rotation_angle_x,
                      self.action_param.rotation_angle_y,
                      self.action_param.rotation_angle_z,
                  )
              augmentation_layers.append(rotation_layer)
  
      volume_padding_layer = [
          PadLayer(
              image_name=SUPPORTED_INPUT,
              border=self.net_param.volume_padding_size,
              mode=self.net_param.volume_padding_mode,
              pad_to=self.net_param.volume_padding_to_size,
          )
      ]
  
      for reader in self.readers:
          reader.add_preprocessing_layers(
              volume_padding_layer
              + normalisation_layers
              + augmentation_layers
          )
  
imports: import tensorflow
  from niftynet.application.base_application import BaseApplication
  from niftynet.contrib.segmentation_selective_sampler.sampler_selective import SelectiveSampler, Constraint
  from niftynet.engine.application_factory import ApplicationNetFactory, InitializerFactory, OptimiserFactory
  from niftynet.engine.application_variables import CONSOLE, NETWORK_OUTPUT, TF_SUMMARIES
  from niftynet.engine.sampler_grid_v2 import GridSampler
  from niftynet.engine.windows_aggregator_grid import GridSamplesAggregator
  from niftynet.io.image_reader import ImageReader
  from niftynet.layer.binary_masking import BinaryMaskingLayer
  from niftynet.layer.discrete_label_normalisation import DiscreteLabelNormalisationLayer
  from niftynet.layer.histogram_normalisation import HistogramNormalisationLayer
  from niftynet.layer.loss_segmentation import LossFunction
  from niftynet.layer.mean_variance_normalisation import MeanVarNormalisationLayer
  from niftynet.layer.pad import PadLayer
  from niftynet.layer.post_processing import PostProcessingLayer
  from niftynet.layer.rand_flip import RandomFlipLayer
  from niftynet.layer.rand_rotation import RandomRotationLayer
  from niftynet.layer.rand_spatial_scaling import RandomSpatialScalingLayer
label_desc: Use image node_id 2 for calling the SelectiveSampling obj's underlying member method code with example usage: obj.initialise_dataset_loader(data_param, task_param, data_partitioner) without return types